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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2ff0d3fa97a96e373415830bf7e09ae0CVF · CVPR 2026

CoLoR: The Devil is in Scene Coordinate Regression for Large-Scale Visual Localization

Mao, Xindong, Li, Hang, Wu, Yuchen, Li, Jiahe, Bai, Xiao, Zheng, Jin

原文摘要

Scene Coordinate Regression (SCR) has emerged as a memory-efficient paradigm for visual localization. While SCR has demonstrated performance comparable to classic feature matching based approaches in small-scale scenes, it has consistently underperformed in large-scale environments. Large-scale localization is hampered by two challenges: sparse co-visibility and local appearance ambiguity. In this work, we propose CoLoR, a novel training framework tailored for large-scale SCR. First, we explicitly and efficiently partition scene points into multi-view and single-view sets and introduce a two-stage bootstrapping paradigm to provide complete and strong supervision for all points. Second, we propose a multi-granularity retrieval feature, which unifies the conventional global and local features as retrieval-oriented representations at the image and pixel levels, respectively, to enforce feature consistency. Our method achieves state-of-the-art performance on multiple challenging large-scale datasets and significantly narrows the accuracy gap with classical feature matching based approaches while retaining a compact map size.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:487464b7bd108eb2b72a8d068e7c1448CVF · CVPR 2026

HyperGait: Unleashing the Power of Parsing for Gait Recognition in the Wild via Hypergraph

Zheng, Jinkai, Wei, Jiaqing, Jin, Xinxiang, Sun, Yaoqi, Sheng, Xichun, Li, Ming, Qu, Liangqiong, Liu, Xinchen 等 9 位作者

原文摘要

In recent years, the gait parsing sequence has become increasingly popular due to its higher information entropy than the binary silhouette and the keypoint-based skeleton. However, existing parsing-based gait recognition methods have not fully explored the complex, non-linear relationships between features at different positions, semantic, and temporal dynamics levels, i.e., higher-order correlations. To unleash the power of parsing between human body parts and temporal dynamics, this paper proposes a novel hypergraph-based gait recognition framework, named HyperGait. The HyperGait contains a global head and two elaborately-designed modules. In particular, the Spatial Hypergraph Convolutional Module (SHCM) and the Temporal Hypergraph Convolutional Module (THCM) are designed to explore the high-order spatial-level and temporal-level features, respectively.The SHCM extracts fine-grained relationships between human body parts through the hypergraph.The THCM performs the high-order temporal information between temporally related human body parts.Comprehensive experiments on two large-scale gait datasets, i.e., Gait3D and SUSTech1K, show the superior performance of our proposed HyperGait.In highly challenging real-world scenarios, with only parsing as input, our HyperGait achieves the Rank-1 accuracy of 80.5% on the Gait3D dataset.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:662e9cf3725204586bfb8f3f64b1d22cCVF · CVPR 2026

ReFTA: Breaking the Weight Reconstruction Bottleneck in Tensorized Parameter-Efficient Fine-Tuning

Zheng, Jingjing, Tang, Anda, Mao, Qiangqiang, Lin, Zhouchen, Cao, Yankai

原文摘要

Tensor-based methods have attracted growing interest due to their ability to reduce trainable parameters and offer advantages over matrix-based approaches in parameter-efficient fine-tuning (e.g., LoRA and PiSSA), particularly in capturing inter-layer correlations. However, directly applying tensor methods requires repeated reconstruction of model weights during training, introducing substantial overhead and becoming a key bottleneck for deployment. To address this limitation, we propose Reconstruction-Free Tensor-Based Adaptation (ReFTA), which offers four advantages: (1) eliminating weight tensor reconstruction via tensor algebraic properties; (2) reducing initialization quantization error by fine-tuning only the principal tensor components; (3) providing a rigorous generalization guarantee rooted in tensor product algebra; and (4) enabling a unified design controlled by a single tensor rank configuration. Extensive experiments on both image classification (IC) and natural language understanding (NLU) tasks demonstrate that ReFTA achieves the best accuracy-efficiency trade-off among all evaluated methods. Across most cases, ReFTA attains the highest average accuracy with the fewest trainable parameters. On IC tasks with the ViT-Large model, ReFTA surpasses LoRA by 5.6% in average accuracy, while reducing the number of trainable parameters by up to 96% compared to LoRA. On NLU tasks with RoBERTa-Large, ReFTA improves the average accuracy by approximately 5% over most existing methods while using only 86.4% fewer parameters than LoRA (r=1) and 97.5% fewer than PiSSA. The code is available at https://github.com/jzheng20/ReFTA.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:7a2c2a6c8e5c87b4e271dc743e6c3d03CVF · CVPR 2026

Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFT

Liu, Yesheng, Li, Hao, Xu, Haiyu, Pei, Baoqi, Wang, Jiahao, Zhao, Mingxuan, Zheng, Jing-Shu, He, Zheqi 等 12 位作者

原文摘要

Multiple-choice question answering (MCQA) has been a popular format for evaluating and reinforcement fine-tuning (RFT) of modern multimodal language models. Its constrained output format allows for simplified, deterministic automatic verification.However, we find that the options may leak exploitable signals, which makes the accuracy metrics unreliable for indicating real capabilities and encourages explicit or implicit answer guessing behaviors during RFT. We propose ReVeL (Rewrite and Verify by LLM), a framework that rewrites multiple-choice questions into open-form questions while keeping answers verifiable whenever possible. The framework categorizes questions according to different answer types, apply different rewriting and verification schemes, respectively. When applied for RFT, we converted 20k MCQA examples and use GRPO to finetune Qwen2.5-VL models. Models trained on ReVeL-OpenQA match MCQA accuracy on multiple-choice benchmarks and improve OpenQA accuracy by about six percentage points, indicating better data efficiency and more robust reward signals than MCQA-based training. When used for evaluation, ReVeL also reveals up to 20 percentage points of score inflation in MCQA benchmarks (relative to OpenQA), improves judging accuracy, and reduces both cost and latency. We will release code and data publicly.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:dd2ac9be5bea6370f73f5313de5345c7CVF · CVPR 2026

SCE-SLAM: Scale-Consistent Monocular SLAM via Scene Coordinate Embeddings

Wu, Yuchen, Li, Jiahe, Yu, Xiaohan, Yu, Lina, Zheng, Jin, Bai, Xiao

原文摘要

Monocular visual SLAM enables 3D reconstruction from internet video and autonomous navigation on resource-constrained platforms, yet suffers from scale drift, i.e., the gradual divergence of estimated scale over long sequences. Existing frame-to-frame methods achieve real-time performance through local optimization but accumulate scale drift due to the lack of global constraints among independent windows. To address this, we propose SCE-SLAM, an end-to-end SLAM system that maintains scale consistency through scene coordinate embeddings, which are learned patch-level representations encoding 3D geometric relationships under a canonical scale reference. The framework consists of two key modules: geometry-guided aggregation that leverages 3D spatial proximity to propagate scale information from historical observations through geometry-modulated attention, and scene coordinate bundle adjustment that anchors current estimates to the reference scale through explicit 3D coordinate constraints decoded from the scene coordinate embeddings. Experiments on KITTI, Waymo, and vKITTI demonstrate substantial improvements: our method reduces absolute trajectory error by 8.36m on KITTI compared to the best prior approach, while maintaining 36 FPS and achieving scale consistency across large-scale scenes.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ede7845deb96e3e3960b0b06d47c6862CVF · CVPR 2026

JUMP-Hand: Learning Joint-wise Uncertainty to Gate Mixture of View Experts for Multi-View 3D Hand Reconstruction

Kuang, Haohong, Xiao, Yang, Jiang, Changlong, Zheng, Jinghong, Xu, Hang, Wang, Ran, Cao, Zhiguo, Zhou, Joey Tianyi

原文摘要

We propose JUMP-Hand, a novel multi-view 3D hand reconstruction method that explicitly models probabilistic joint-wise uncertainty as a gating mechanism for multi-view fusion. Existing approaches usually rely on naive pooling or implicit attention, overlooking that each hand joint exhibits varying visibility and reliability across views. Such indiscriminate aggregation of noisy or unreliable information inevitably degrades overall performance. For instance, a joint severely occluded in one view might be clearly visible in another. To address this, JUMP-Hand draws inspiration from the Mixture of Experts (MoE) paradigm, treating each 2D view as a specialized expert. The key idea is that the reliability of each view expert is quantified through joint-wise uncertainty modeling, serving as an explicit gating signal to route experts' partial yet complementary clues for each joint in a coarse-to-fine reconstruction paradigm. In this design, uncertainty not only guides the uncertainty-aware triangulation for reliable 3D hand initialization during the coarse stage, but also acts as a gating signal during the refinement stage to adaptively aggregate multi-scale features from different view experts on a joint-wise basis, enabling robust 3D hand reconstruction. Extensive experiments on DexYCB-MV, HO3D-MV, and OakInk-MV demonstrate that our method consistently achieves state-of-the-art results, validating the effectiveness of the proposed method with joint-wise uncertainty gating for reliable 3D hand reconstruction. Code is available at https://github.com/HaohongKuang/JUMP-Hand.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:f50a4be0e84273c794bd1591080a580aCVF · CVPR 2026

Pip-Stereo: Progressive Iterations Pruner for Iterative Optimization based Stereo Matching

Zheng, Jintu, Liu, Qizhe, Xu, Huangxin, Chen, Zhuojie

原文摘要

While iterative stereo matching achieves high accuracy, its dependence on Recurrent Neural Networks (RNN) hinders edge deployment, a challenge underexplored in existing researches. We analyze iterative refinement and reveal that disparity updates are spatially sparse and temporally redundant. First, we introduce a progressive iteration pruning strategy that suppresses redundant update steps, effectively collapsing the recursive computation into a near-single-pass inference. Second, we propose a collaborative monocular prior transfer framework that implicitly embeds depth priors without requiring a dedicated monocular encoder, thereby eliminating its associated computational burden. Third, we develop FlashGRU, a hardware-aware RNN operator leveraging structured sparsity and I/O-conscious design, achieving a 7.28xspeedup, 76.6% memory peak reduction and 80.9% global memory requests reduction over natvie ConvGRUs under 2K resolution. Our PipStereo enables real-time, high-fidelity stereo matching on edge hardware: it processes 320x640 frames in just 75ms on an NVIDIA Jetson Orin NX (FP16) and 19ms on RTX 4090, matching the accuracy of large iterative based models, and our generalization ability and accuracy far exceeds that of existing real-time methods.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗